<<<<<<< HEAD My Data Report

Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations548535
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory375.1 MiB
Average record size in memory717.0 B

Variable types

Numeric5
Text6
Categorical4

Alerts

body is highly overall correlated with transmissionHigh correlation
color is highly overall correlated with transmissionHigh correlation
mmr is highly overall correlated with odometer and 2 other fieldsHigh correlation
odometer is highly overall correlated with mmr and 2 other fieldsHigh correlation
sellingprice is highly overall correlated with mmr and 2 other fieldsHigh correlation
transmission is highly overall correlated with body and 1 other fieldsHigh correlation
year is highly overall correlated with mmr and 2 other fieldsHigh correlation
body is highly imbalanced (52.4%) Imbalance
transmission is highly imbalanced (87.3%) Imbalance
interior is highly imbalanced (50.5%) Imbalance

Reproduction

Analysis started2025-07-10 12:48:27.561058
Analysis finished2025-07-10 12:49:11.320243
Duration43.76 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

year
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.1151
Minimum1984
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-07-10T18:19:11.573544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1984
5-th percentile2002
Q12008
median2012
Q32013
95-th percentile2014
Maximum2015
Range31
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.9091265
Coefficient of variation (CV)0.0019447277
Kurtosis0.97970829
Mean2010.1151
Median Absolute Deviation (MAD)2
Skewness-1.1909475
Sum1.1026185 × 109
Variance15.28127
MonotonicityNot monotonic
2025-07-10T18:19:11.839580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2012 101380
18.5%
2013 98113
17.9%
2014 80574
14.7%
2011 48216
8.8%
2008 31055
 
5.7%
2007 29495
 
5.4%
2010 25986
 
4.7%
2006 25421
 
4.6%
2009 20411
 
3.7%
2005 20280
 
3.7%
Other values (21) 67604
12.3%
ValueCountFrequency (%)
1984 1
 
< 0.1%
1985 2
 
< 0.1%
1986 2
 
< 0.1%
1987 1
 
< 0.1%
1989 6
 
< 0.1%
1990 45
 
< 0.1%
1991 65
 
< 0.1%
1992 123
 
< 0.1%
1993 177
< 0.1%
1994 358
0.1%
ValueCountFrequency (%)
2015 9223
 
1.7%
2014 80574
14.7%
2013 98113
17.9%
2012 101380
18.5%
2011 48216
8.8%
2010 25986
 
4.7%
2009 20411
 
3.7%
2008 31055
 
5.7%
2007 29495
 
5.4%
2006 25421
 
4.6%

make
Text

Distinct96
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.0 MiB
2025-07-10T18:19:12.167147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.9952236
Min length2

Characters and Unicode

Total characters3288590
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowKia
2nd rowKia
3rd rowBMW
4th rowVolvo
5th rowBMW
ValueCountFrequency (%)
ford 94001
17.1%
chevrolet 60587
 
11.0%
nissan 54017
 
9.8%
toyota 39966
 
7.3%
dodge 30956
 
5.6%
honda 27351
 
5.0%
hyundai 21837
 
4.0%
bmw 20793
 
3.8%
kia 18084
 
3.3%
chrysler 17485
 
3.2%
Other values (54) 165366
30.0%
2025-07-10T18:19:12.563778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 328819
 
10.0%
e 300106
 
9.1%
a 235578
 
7.2%
r 230199
 
7.0%
d 215895
 
6.6%
n 186317
 
5.7%
i 184908
 
5.6%
s 178494
 
5.4%
t 128322
 
3.9%
l 116781
 
3.6%
Other values (39) 1183171
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3288590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 328819
 
10.0%
e 300106
 
9.1%
a 235578
 
7.2%
r 230199
 
7.0%
d 215895
 
6.6%
n 186317
 
5.7%
i 184908
 
5.6%
s 178494
 
5.4%
t 128322
 
3.9%
l 116781
 
3.6%
Other values (39) 1183171
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3288590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 328819
 
10.0%
e 300106
 
9.1%
a 235578
 
7.2%
r 230199
 
7.0%
d 215895
 
6.6%
n 186317
 
5.7%
i 184908
 
5.6%
s 178494
 
5.4%
t 128322
 
3.9%
l 116781
 
3.6%
Other values (39) 1183171
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3288590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 328819
 
10.0%
e 300106
 
9.1%
a 235578
 
7.2%
r 230199
 
7.0%
d 215895
 
6.6%
n 186317
 
5.7%
i 184908
 
5.6%
s 178494
 
5.4%
t 128322
 
3.9%
l 116781
 
3.6%
Other values (39) 1183171
36.0%

model
Text

Distinct851
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size33.4 MiB
2025-07-10T18:19:12.923237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length23
Mean length6.7669192
Min length1

Characters and Unicode

Total characters3711892
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)< 0.1%

Sample

1st rowsorento
2nd rowsorento
3rd row3 series
4th rows60
5th row6 series gran coupe
ValueCountFrequency (%)
altima 19432
 
2.9%
series 15515
 
2.3%
grand 14928
 
2.2%
f-150 14527
 
2.2%
1500 14476
 
2.2%
fusion 13639
 
2.0%
camry 13515
 
2.0%
escape 12027
 
1.8%
focus 10463
 
1.6%
g 9333
 
1.4%
Other values (740) 531442
79.4%
2025-07-10T18:19:13.551797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 426799
 
11.5%
e 331192
 
8.9%
r 309170
 
8.3%
s 253866
 
6.8%
o 206541
 
5.6%
c 199372
 
5.4%
n 188769
 
5.1%
i 184780
 
5.0%
t 173535
 
4.7%
l 153434
 
4.1%
Other values (30) 1284434
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3711892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 426799
 
11.5%
e 331192
 
8.9%
r 309170
 
8.3%
s 253866
 
6.8%
o 206541
 
5.6%
c 199372
 
5.4%
n 188769
 
5.1%
i 184780
 
5.0%
t 173535
 
4.7%
l 153434
 
4.1%
Other values (30) 1284434
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3711892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 426799
 
11.5%
e 331192
 
8.9%
r 309170
 
8.3%
s 253866
 
6.8%
o 206541
 
5.6%
c 199372
 
5.4%
n 188769
 
5.1%
i 184780
 
5.0%
t 173535
 
4.7%
l 153434
 
4.1%
Other values (30) 1284434
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3711892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 426799
 
11.5%
e 331192
 
8.9%
r 309170
 
8.3%
s 253866
 
6.8%
o 206541
 
5.6%
c 199372
 
5.4%
n 188769
 
5.1%
i 184780
 
5.0%
t 173535
 
4.7%
l 153434
 
4.1%
Other values (30) 1284434
34.6%

trim
Text

Distinct1963
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size32.3 MiB
2025-07-10T18:19:13.910608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length46
Median length37
Mean length4.736407
Min length1

Characters and Unicode

Total characters2598085
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique241 ?
Unique (%)< 0.1%

Sample

1st rowLX
2nd rowLX
3rd row328i SULEV
4th rowT5
5th row650i
ValueCountFrequency (%)
base 56196
 
8.4%
se 48404
 
7.2%
s 30314
 
4.5%
lx 21394
 
3.2%
limited 20585
 
3.1%
lt 20263
 
3.0%
2.5 18864
 
2.8%
xlt 18797
 
2.8%
ls 17937
 
2.7%
sport 17625
 
2.6%
Other values (963) 402466
59.8%
2025-07-10T18:19:14.526249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 215102
 
8.3%
S 206547
 
7.9%
e 155289
 
6.0%
i 135294
 
5.2%
E 127072
 
4.9%
124311
 
4.8%
T 120964
 
4.7%
a 109025
 
4.2%
r 97936
 
3.8%
X 91570
 
3.5%
Other values (62) 1214975
46.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2598085
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 215102
 
8.3%
S 206547
 
7.9%
e 155289
 
6.0%
i 135294
 
5.2%
E 127072
 
4.9%
124311
 
4.8%
T 120964
 
4.7%
a 109025
 
4.2%
r 97936
 
3.8%
X 91570
 
3.5%
Other values (62) 1214975
46.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2598085
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 215102
 
8.3%
S 206547
 
7.9%
e 155289
 
6.0%
i 135294
 
5.2%
E 127072
 
4.9%
124311
 
4.8%
T 120964
 
4.7%
a 109025
 
4.2%
r 97936
 
3.8%
X 91570
 
3.5%
Other values (62) 1214975
46.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2598085
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 215102
 
8.3%
S 206547
 
7.9%
e 155289
 
6.0%
i 135294
 
5.2%
E 127072
 
4.9%
124311
 
4.8%
T 120964
 
4.7%
a 109025
 
4.2%
r 97936
 
3.8%
X 91570
 
3.5%
Other values (62) 1214975
46.8%

body
Categorical

High correlation  Imbalance 

Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
sedan
244235 
suv
143844 
hatchback
26238 
minivan
25529 
coupe
 
17752
Other values (41)
90937 

Length

Max length23
Median length5
Mean length5.2778073
Min length3

Characters and Unicode

Total characters2895062
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowsuv
2nd rowsuv
3rd rowsedan
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 244235
44.5%
suv 143844
26.2%
hatchback 26238
 
4.8%
minivan 25529
 
4.7%
coupe 17752
 
3.2%
crew cab 16394
 
3.0%
wagon 16129
 
2.9%
convertible 10476
 
1.9%
supercrew 9033
 
1.6%
g sedan 7417
 
1.4%
Other values (36) 31488
 
5.7%

Length

2025-07-10T18:19:14.667336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sedan 251652
42.4%
suv 143844
24.2%
cab 33137
 
5.6%
hatchback 26238
 
4.4%
minivan 25529
 
4.3%
coupe 19983
 
3.4%
crew 16394
 
2.8%
wagon 16180
 
2.7%
convertible 10933
 
1.8%
g 9333
 
1.6%
Other values (33) 40608
 
6.8%

Most occurring characters

ValueCountFrequency (%)
s 415043
14.3%
a 401157
13.9%
e 360625
12.5%
n 341773
11.8%
d 266362
9.2%
u 189113
6.5%
v 186798
6.5%
c 148924
 
5.1%
b 77516
 
2.7%
i 64724
 
2.2%
Other values (19) 443027
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2895062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 415043
14.3%
a 401157
13.9%
e 360625
12.5%
n 341773
11.8%
d 266362
9.2%
u 189113
6.5%
v 186798
6.5%
c 148924
 
5.1%
b 77516
 
2.7%
i 64724
 
2.2%
Other values (19) 443027
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2895062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 415043
14.3%
a 401157
13.9%
e 360625
12.5%
n 341773
11.8%
d 266362
9.2%
u 189113
6.5%
v 186798
6.5%
c 148924
 
5.1%
b 77516
 
2.7%
i 64724
 
2.2%
Other values (19) 443027
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2895062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 415043
14.3%
a 401157
13.9%
e 360625
12.5%
n 341773
11.8%
d 266362
9.2%
u 189113
6.5%
v 186798
6.5%
c 148924
 
5.1%
b 77516
 
2.7%
i 64724
 
2.2%
Other values (19) 443027
15.3%

transmission
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
automatic
531434 
manual
 
17075
sedan
 
26

Length

Max length9
Median length9
Mean length8.9064253
Min length5

Characters and Unicode

Total characters4885486
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowautomatic
2nd rowautomatic
3rd rowautomatic
4th rowautomatic
5th rowautomatic

Common Values

ValueCountFrequency (%)
automatic 531434
96.9%
manual 17075
 
3.1%
sedan 26
 
< 0.1%

Length

2025-07-10T18:19:14.830600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T18:19:14.950664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
automatic 531434
96.9%
manual 17075
 
3.1%
sedan 26
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 1097044
22.5%
t 1062868
21.8%
u 548509
11.2%
m 548509
11.2%
o 531434
10.9%
i 531434
10.9%
c 531434
10.9%
n 17101
 
0.4%
l 17075
 
0.3%
s 26
 
< 0.1%
Other values (2) 52
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4885486
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1097044
22.5%
t 1062868
21.8%
u 548509
11.2%
m 548509
11.2%
o 531434
10.9%
i 531434
10.9%
c 531434
10.9%
n 17101
 
0.4%
l 17075
 
0.3%
s 26
 
< 0.1%
Other values (2) 52
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4885486
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1097044
22.5%
t 1062868
21.8%
u 548509
11.2%
m 548509
11.2%
o 531434
10.9%
i 531434
10.9%
c 531434
10.9%
n 17101
 
0.4%
l 17075
 
0.3%
s 26
 
< 0.1%
Other values (2) 52
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4885486
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1097044
22.5%
t 1062868
21.8%
u 548509
11.2%
m 548509
11.2%
o 531434
10.9%
i 531434
10.9%
c 531434
10.9%
n 17101
 
0.4%
l 17075
 
0.3%
s 26
 
< 0.1%
Other values (2) 52
 
< 0.1%

state
Text

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.9 MiB
2025-07-10T18:19:15.200343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length2
Mean length2.000711
Min length2

Characters and Unicode

Total characters1097460
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st rowca
2nd rowca
3rd rowca
4th rowca
5th rowca
ValueCountFrequency (%)
fl 81482
14.9%
ca 71667
13.1%
pa 53158
 
9.7%
tx 45196
 
8.2%
ga 34059
 
6.2%
nj 27365
 
5.0%
il 23196
 
4.2%
nc 21333
 
3.9%
oh 21252
 
3.9%
tn 20691
 
3.8%
Other values (54) 149136
27.2%
2025-07-10T18:19:15.567205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 195954
17.9%
n 108197
9.9%
l 106867
9.7%
c 105915
9.7%
f 81508
 
7.4%
t 67685
 
6.2%
m 59757
 
5.4%
p 55831
 
5.1%
i 53682
 
4.9%
o 48924
 
4.5%
Other values (26) 213140
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1097460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 195954
17.9%
n 108197
9.9%
l 106867
9.7%
c 105915
9.7%
f 81508
 
7.4%
t 67685
 
6.2%
m 59757
 
5.4%
p 55831
 
5.1%
i 53682
 
4.9%
o 48924
 
4.5%
Other values (26) 213140
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1097460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 195954
17.9%
n 108197
9.9%
l 106867
9.7%
c 105915
9.7%
f 81508
 
7.4%
t 67685
 
6.2%
m 59757
 
5.4%
p 55831
 
5.1%
i 53682
 
4.9%
o 48924
 
4.5%
Other values (26) 213140
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1097460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 195954
17.9%
n 108197
9.9%
l 106867
9.7%
c 105915
9.7%
f 81508
 
7.4%
t 67685
 
6.2%
m 59757
 
5.4%
p 55831
 
5.1%
i 53682
 
4.9%
o 48924
 
4.5%
Other values (26) 213140
19.4%

condition
Real number (ℝ)

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.631234
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-07-10T18:19:15.702041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q123
median34
Q341
95-th percentile47
Maximum49
Range48
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.401857
Coefficient of variation (CV)0.43752259
Kurtosis-0.2304798
Mean30.631234
Median Absolute Deviation (MAD)8
Skewness-0.82779016
Sum16802304
Variance179.60976
MonotonicityNot monotonic
2025-07-10T18:19:15.866416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
19 43050
 
7.8%
35 26808
 
4.9%
37 25902
 
4.7%
44 25463
 
4.6%
43 24925
 
4.5%
42 24333
 
4.4%
36 23174
 
4.2%
41 23038
 
4.2%
2 21108
 
3.8%
39 19937
 
3.6%
Other values (31) 290797
53.0%
ValueCountFrequency (%)
1 7458
 
1.4%
2 21108
3.8%
3 10772
2.0%
4 19796
3.6%
5 11154
2.0%
11 91
 
< 0.1%
12 97
 
< 0.1%
13 79
 
< 0.1%
14 138
 
< 0.1%
15 144
 
< 0.1%
ValueCountFrequency (%)
49 13038
2.4%
48 12686
2.3%
47 11346
2.1%
46 12616
2.3%
45 12273
2.2%
44 25463
4.6%
43 24925
4.5%
42 24333
4.4%
41 23038
4.2%
39 19937
3.6%

odometer
Real number (ℝ)

High correlation 

Distinct170099
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67533.923
Minimum1
Maximum999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-07-10T18:19:16.032894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10449
Q128133
median51402
Q397926.5
95-th percentile168655.2
Maximum999999
Range999998
Interquartile range (IQR)69793.5

Descriptive statistics

Standard deviation52917.195
Coefficient of variation (CV)0.78356466
Kurtosis13.803513
Mean67533.923
Median Absolute Deviation (MAD)29819
Skewness1.8627155
Sum3.7044721 × 1010
Variance2.8002296 × 109
MonotonicityNot monotonic
2025-07-10T18:19:16.209606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1254
 
0.2%
999999 69
 
< 0.1%
10 29
 
< 0.1%
21587 21
 
< 0.1%
2 18
 
< 0.1%
29137 18
 
< 0.1%
21310 18
 
< 0.1%
8 18
 
< 0.1%
31258 17
 
< 0.1%
24023 17
 
< 0.1%
Other values (170089) 547056
99.7%
ValueCountFrequency (%)
1 1254
0.2%
2 18
 
< 0.1%
3 9
 
< 0.1%
4 9
 
< 0.1%
5 17
 
< 0.1%
6 13
 
< 0.1%
7 13
 
< 0.1%
8 18
 
< 0.1%
9 11
 
< 0.1%
10 29
 
< 0.1%
ValueCountFrequency (%)
999999 69
< 0.1%
980113 1
 
< 0.1%
959276 1
 
< 0.1%
694978 2
 
< 0.1%
621388 1
 
< 0.1%
580956 1
 
< 0.1%
537334 1
 
< 0.1%
522212 1
 
< 0.1%
500227 1
 
< 0.1%
495757 1
 
< 0.1%

color
Categorical

High correlation 

Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.9 MiB
black
109300 
white
104426 
silver
81960 
gray
81956 
blue
50106 
Other values (41)
120787 

Length

Max length9
Median length8
Mean length4.6244889
Min length1

Characters and Unicode

Total characters2536694
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st rowwhite
2nd rowwhite
3rd rowgray
4th rowwhite
5th rowgray

Common Values

ValueCountFrequency (%)
black 109300
19.9%
white 104426
19.0%
silver 81960
14.9%
gray 81956
14.9%
blue 50106
9.1%
red 42811
 
7.8%
24592
 
4.5%
gold 11006
 
2.0%
green 10960
 
2.0%
beige 8998
 
1.6%
Other values (36) 22420
 
4.1%

Length

2025-07-10T18:19:16.382653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black 109300
19.9%
white 104426
19.0%
silver 81960
14.9%
gray 81956
14.9%
blue 50106
9.1%
red 42811
 
7.8%
24592
 
4.5%
gold 11006
 
2.0%
green 10960
 
2.0%
beige 8998
 
1.6%
Other values (36) 22420
 
4.1%

Most occurring characters

ValueCountFrequency (%)
e 325652
12.8%
l 256860
 
10.1%
r 237366
 
9.4%
i 197087
 
7.8%
a 194211
 
7.7%
b 183850
 
7.2%
g 123735
 
4.9%
w 113724
 
4.5%
c 110246
 
4.3%
k 109342
 
4.3%
Other values (25) 684621
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2536694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 325652
12.8%
l 256860
 
10.1%
r 237366
 
9.4%
i 197087
 
7.8%
a 194211
 
7.7%
b 183850
 
7.2%
g 123735
 
4.9%
w 113724
 
4.5%
c 110246
 
4.3%
k 109342
 
4.3%
Other values (25) 684621
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2536694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 325652
12.8%
l 256860
 
10.1%
r 237366
 
9.4%
i 197087
 
7.8%
a 194211
 
7.7%
b 183850
 
7.2%
g 123735
 
4.9%
w 113724
 
4.5%
c 110246
 
4.3%
k 109342
 
4.3%
Other values (25) 684621
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2536694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 325652
12.8%
l 256860
 
10.1%
r 237366
 
9.4%
i 197087
 
7.8%
a 194211
 
7.7%
b 183850
 
7.2%
g 123735
 
4.9%
w 113724
 
4.5%
c 110246
 
4.3%
k 109342
 
4.3%
Other values (25) 684621
27.0%

interior
Categorical

Imbalance 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
black
241298 
gray
174741 
beige
58679 
tan
43162 
 
16738
Other values (12)
 
13917

Length

Max length9
Median length5
Mean length4.4020673
Min length1

Characters and Unicode

Total characters2414688
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblack
2nd rowbeige
3rd rowblack
4th rowblack
5th rowblack

Common Values

ValueCountFrequency (%)
black 241298
44.0%
gray 174741
31.9%
beige 58679
 
10.7%
tan 43162
 
7.9%
16738
 
3.1%
brown 8474
 
1.5%
red 1329
 
0.2%
silver 1083
 
0.2%
blue 1070
 
0.2%
off-white 485
 
0.1%
Other values (7) 1476
 
0.3%

Length

2025-07-10T18:19:16.535147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black 241298
44.0%
gray 174741
31.9%
beige 58679
 
10.7%
tan 43162
 
7.9%
16738
 
3.1%
brown 8474
 
1.5%
red 1329
 
0.2%
silver 1083
 
0.2%
blue 1070
 
0.2%
off-white 485
 
0.1%
Other values (7) 1476
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 459337
19.0%
b 309708
12.8%
l 244139
10.1%
c 241298
10.0%
k 241298
10.0%
g 234292
9.7%
r 186517
7.7%
y 174948
 
7.2%
e 122533
 
5.1%
i 60498
 
2.5%
Other values (13) 140120
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2414688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 459337
19.0%
b 309708
12.8%
l 244139
10.1%
c 241298
10.0%
k 241298
10.0%
g 234292
9.7%
r 186517
7.7%
y 174948
 
7.2%
e 122533
 
5.1%
i 60498
 
2.5%
Other values (13) 140120
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2414688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 459337
19.0%
b 309708
12.8%
l 244139
10.1%
c 241298
10.0%
k 241298
10.0%
g 234292
9.7%
r 186517
7.7%
y 174948
 
7.2%
e 122533
 
5.1%
i 60498
 
2.5%
Other values (13) 140120
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2414688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 459337
19.0%
b 309708
12.8%
l 244139
10.1%
c 241298
10.0%
k 241298
10.0%
g 234292
9.7%
r 186517
7.7%
y 174948
 
7.2%
e 122533
 
5.1%
i 60498
 
2.5%
Other values (13) 140120
 
5.8%

seller
Text

Distinct14084
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size41.8 MiB
2025-07-10T18:19:16.958866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length42
Mean length22.996901
Min length3

Characters and Unicode

Total characters12614605
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4888 ?
Unique (%)0.9%

Sample

1st rowkia motors america inc
2nd rowkia motors america inc
3rd rowfinancial services remarketing (lease)
4th rowvolvo na rep/world omni
5th rowfinancial services remarketing (lease)
ValueCountFrequency (%)
inc 84687
 
4.6%
corporation 47535
 
2.6%
services 47406
 
2.6%
auto 46272
 
2.5%
credit 46249
 
2.5%
motor 45605
 
2.5%
llc 45067
 
2.4%
financial 43492
 
2.4%
ford 35890
 
1.9%
remarketing 34367
 
1.9%
Other values (8488) 1369096
74.2%
2025-07-10T18:19:17.647933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1315618
 
10.4%
e 1123791
 
8.9%
a 1032357
 
8.2%
r 945141
 
7.5%
n 937696
 
7.4%
i 902124
 
7.2%
o 848435
 
6.7%
t 782371
 
6.2%
c 722194
 
5.7%
s 659071
 
5.2%
Other values (37) 3345807
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12614605
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1315618
 
10.4%
e 1123791
 
8.9%
a 1032357
 
8.2%
r 945141
 
7.5%
n 937696
 
7.4%
i 902124
 
7.2%
o 848435
 
6.7%
t 782371
 
6.2%
c 722194
 
5.7%
s 659071
 
5.2%
Other values (37) 3345807
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12614605
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1315618
 
10.4%
e 1123791
 
8.9%
a 1032357
 
8.2%
r 945141
 
7.5%
n 937696
 
7.4%
i 902124
 
7.2%
o 848435
 
6.7%
t 782371
 
6.2%
c 722194
 
5.7%
s 659071
 
5.2%
Other values (37) 3345807
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12614605
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1315618
 
10.4%
e 1123791
 
8.9%
a 1032357
 
8.2%
r 945141
 
7.5%
n 937696
 
7.4%
i 902124
 
7.2%
o 848435
 
6.7%
t 782371
 
6.2%
c 722194
 
5.7%
s 659071
 
5.2%
Other values (37) 3345807
26.5%

mmr
Real number (ℝ)

High correlation 

Distinct1102
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13857.926
Minimum25
Maximum182000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-07-10T18:19:17.843868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile1850
Q17275
median12350
Q318400
95-th percentile30600
Maximum182000
Range181975
Interquartile range (IQR)11125

Descriptive statistics

Standard deviation9655.8055
Coefficient of variation (CV)0.69677132
Kurtosis11.657457
Mean13857.926
Median Absolute Deviation (MAD)5525
Skewness2.0085911
Sum7.6015575 × 109
Variance93234579
MonotonicityNot monotonic
2025-07-10T18:19:18.014594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12500 1748
 
0.3%
11600 1739
 
0.3%
11650 1734
 
0.3%
12150 1714
 
0.3%
11850 1707
 
0.3%
11300 1704
 
0.3%
11750 1692
 
0.3%
12350 1686
 
0.3%
12700 1683
 
0.3%
12200 1677
 
0.3%
Other values (1092) 531451
96.9%
ValueCountFrequency (%)
25 27
 
< 0.1%
50 41
< 0.1%
75 22
 
< 0.1%
100 32
< 0.1%
125 34
< 0.1%
150 42
< 0.1%
175 63
< 0.1%
200 51
< 0.1%
225 56
< 0.1%
250 75
< 0.1%
ValueCountFrequency (%)
182000 1
 
< 0.1%
178000 1
 
< 0.1%
176000 1
 
< 0.1%
172000 1
 
< 0.1%
170000 3
< 0.1%
166000 3
< 0.1%
164000 1
 
< 0.1%
163000 1
 
< 0.1%
162000 1
 
< 0.1%
161000 1
 
< 0.1%

sellingprice
Real number (ℝ)

High correlation 

Distinct1879
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13698.551
Minimum1
Maximum230000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-07-10T18:19:18.209328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1550
Q17000
median12200
Q318300
95-th percentile30600
Maximum230000
Range229999
Interquartile range (IQR)11300

Descriptive statistics

Standard deviation9728.1041
Coefficient of variation (CV)0.71015573
Kurtosis11.315799
Mean13698.551
Median Absolute Deviation (MAD)5600
Skewness1.9641304
Sum7.5141344 × 109
Variance94636010
MonotonicityNot monotonic
2025-07-10T18:19:18.380972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11000 4409
 
0.8%
12000 4395
 
0.8%
13000 4288
 
0.8%
10000 3994
 
0.7%
14000 3864
 
0.7%
11500 3839
 
0.7%
12500 3686
 
0.7%
9000 3622
 
0.7%
10500 3490
 
0.6%
15000 3358
 
0.6%
Other values (1869) 509590
92.9%
ValueCountFrequency (%)
1 4
 
< 0.1%
100 17
 
< 0.1%
150 18
 
< 0.1%
175 10
 
< 0.1%
200 175
 
< 0.1%
225 93
 
< 0.1%
250 256
 
< 0.1%
275 110
 
< 0.1%
300 1168
0.2%
325 196
 
< 0.1%
ValueCountFrequency (%)
230000 1
< 0.1%
183000 1
< 0.1%
173000 1
< 0.1%
171500 1
< 0.1%
169500 1
< 0.1%
169000 1
< 0.1%
167000 1
< 0.1%
165000 2
< 0.1%
163000 2
< 0.1%
161000 1
< 0.1%
Distinct3736
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size50.2 MiB
2025-07-10T18:19:18.780819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length39
Mean length38.998379
Min length4

Characters and Unicode

Total characters21391976
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique600 ?
Unique (%)0.1%

Sample

1st rowTue Dec 16 2014 12:30:00 GMT-0800 (PST)
2nd rowTue Dec 16 2014 12:30:00 GMT-0800 (PST)
3rd rowThu Jan 15 2015 04:30:00 GMT-0800 (PST)
4th rowThu Jan 29 2015 04:30:00 GMT-0800 (PST)
5th rowThu Dec 18 2014 12:30:00 GMT-0800 (PST)
ValueCountFrequency (%)
2015 494872
 
12.9%
pst 388481
 
10.1%
gmt-0800 388481
 
10.1%
wed 163323
 
4.3%
tue 160631
 
4.2%
gmt-0700 160028
 
4.2%
pdt 160028
 
4.2%
feb 159116
 
4.1%
thu 150756
 
3.9%
jan 138734
 
3.6%
Other values (332) 1475139
38.4%
2025-07-10T18:19:19.598786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4730233
22.1%
3291054
15.4%
T 1408405
 
6.6%
: 1097018
 
5.1%
1 1042806
 
4.9%
2 944381
 
4.4%
M 660400
 
3.1%
5 646848
 
3.0%
) 548509
 
2.6%
G 548509
 
2.6%
Other values (30) 6473813
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21391976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4730233
22.1%
3291054
15.4%
T 1408405
 
6.6%
: 1097018
 
5.1%
1 1042806
 
4.9%
2 944381
 
4.4%
M 660400
 
3.1%
5 646848
 
3.0%
) 548509
 
2.6%
G 548509
 
2.6%
Other values (30) 6473813
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21391976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4730233
22.1%
3291054
15.4%
T 1408405
 
6.6%
: 1097018
 
5.1%
1 1042806
 
4.9%
2 944381
 
4.4%
M 660400
 
3.1%
5 646848
 
3.0%
) 548509
 
2.6%
G 548509
 
2.6%
Other values (30) 6473813
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21391976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4730233
22.1%
3291054
15.4%
T 1408405
 
6.6%
: 1097018
 
5.1%
1 1042806
 
4.9%
2 944381
 
4.4%
M 660400
 
3.1%
5 646848
 
3.0%
) 548509
 
2.6%
G 548509
 
2.6%
Other values (30) 6473813
30.3%

Interactions

2025-07-10T18:19:05.974180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:00.149697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:01.691732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:03.027681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:04.509734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:06.260594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:00.596450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:01.961534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:03.329005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:04.844706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:06.583463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:00.878385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:02.213261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:03.579654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:05.125314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:06.840064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:01.145203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:02.480066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:03.910656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:05.427192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:07.149591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:01.442420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:02.736977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:04.245261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T18:19:05.692117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-10T18:19:19.712614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
bodycolorconditioninteriormmrodometersellingpricetransmissionyear
body1.0000.1560.0610.0760.1460.0650.1260.7250.083
color0.1561.0000.0560.1020.0550.0650.0480.7090.090
condition0.0610.0561.0000.0580.428-0.4050.4800.0220.391
interior0.0760.1020.0581.0000.0630.0900.0620.0700.109
mmr0.1460.0550.4280.0631.000-0.7140.9790.0200.692
odometer0.0650.065-0.4050.090-0.7141.000-0.7000.020-0.815
sellingprice0.1260.0480.4800.0620.979-0.7001.0000.0110.674
transmission0.7250.7090.0220.0700.0200.0200.0111.0000.060
year0.0830.0900.3910.1090.692-0.8150.6740.0601.000

Missing values

2025-07-10T18:19:07.772953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-10T18:19:09.022511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

yearmakemodeltrimbodytransmissionstateconditionodometercolorinteriorsellermmrsellingpricesaledate
02015KiasorentoLXsuvautomaticca5.016639.0whiteblackkia motors america inc20500.021500.0Tue Dec 16 2014 12:30:00 GMT-0800 (PST)
12015KiasorentoLXsuvautomaticca5.09393.0whitebeigekia motors america inc20800.021500.0Tue Dec 16 2014 12:30:00 GMT-0800 (PST)
22014BMW3 series328i SULEVsedanautomaticca45.01331.0grayblackfinancial services remarketing (lease)31900.030000.0Thu Jan 15 2015 04:30:00 GMT-0800 (PST)
32015Volvos60T5sedanautomaticca41.014282.0whiteblackvolvo na rep/world omni27500.027750.0Thu Jan 29 2015 04:30:00 GMT-0800 (PST)
42014BMW6 series gran coupe650isedanautomaticca43.02641.0grayblackfinancial services remarketing (lease)66000.067000.0Thu Dec 18 2014 12:30:00 GMT-0800 (PST)
52015Nissanaltima2.5 Ssedanautomaticca1.05554.0grayblackenterprise vehicle exchange / tra / rental / tulsa15350.010900.0Tue Dec 30 2014 12:00:00 GMT-0800 (PST)
62014BMWm5Basesedanautomaticca34.014943.0blackblackthe hertz corporation69000.065000.0Wed Dec 17 2014 12:30:00 GMT-0800 (PST)
72014Chevroletcruze1LTsedanautomaticca2.028617.0blackblackenterprise vehicle exchange / tra / rental / tulsa11900.09800.0Tue Dec 16 2014 13:00:00 GMT-0800 (PST)
82014Audia42.0T Premium Plus quattrosedanautomaticca42.09557.0whiteblackaudi mission viejo32100.032250.0Thu Dec 18 2014 12:00:00 GMT-0800 (PST)
92014ChevroletcamaroLTconvertibleautomaticca3.04809.0redblackd/m auto sales inc26300.017500.0Tue Jan 20 2015 04:00:00 GMT-0800 (PST)
yearmakemodeltrimbodytransmissionstateconditionodometercolorinteriorsellermmrsellingpricesaledate
5485252014Jeepgrand cherokeeLaredosuvautomaticpa42.025180.0grayblackhertz corporation/gdp26000.024500.0Tue Jul 07 2015 06:30:00 GMT-0700 (PDT)
5485262012Dodgegrand caravanAmerican Value Packageminivanautomaticma37.097036.0silvergrayge fleet services for itself/servicer8300.07800.0Tue Jul 07 2015 06:30:00 GMT-0700 (PDT)
5485272012HyundaielantraLimitedsedanautomaticpa4.066720.0graygraychampion mazda10250.010400.0Wed Jul 08 2015 07:30:00 GMT-0700 (PDT)
5485282012Nissansentra2.0 SRsedanautomatictn26.035858.0whitegraynissan-infiniti lt9950.010400.0Wed Jul 08 2015 17:15:00 GMT-0700 (PDT)
5485292011BMW5 series528isedanautomaticfl39.066403.0whitebrownlauderdale imports ltd bmw pembrok pines20300.022800.0Tue Jul 07 2015 06:15:00 GMT-0700 (PDT)
5485302015Kiak900Luxurysedanautomaticin45.018255.0silverblackavis corporation35300.033000.0Thu Jul 09 2015 07:00:00 GMT-0700 (PDT)
5485312012Ram2500Power Wagoncrew cabautomaticwa5.054393.0whiteblacki -5 uhlmann rv30200.030800.0Wed Jul 08 2015 09:30:00 GMT-0700 (PDT)
5485322012BMWx5xDrive35dsuvautomaticca48.050561.0blackblackfinancial services remarketing (lease)29800.034000.0Wed Jul 08 2015 09:30:00 GMT-0700 (PDT)
5485332015Nissanaltima2.5 Ssedanautomaticga38.016658.0whiteblackenterprise vehicle exchange / tra / rental / tulsa15100.011100.0Thu Jul 09 2015 06:45:00 GMT-0700 (PDT)
5485342014Fordf-150XLTsupercrewautomaticca34.015008.0graygrayford motor credit company llc pd29600.026700.0Thu May 28 2015 05:30:00 GMT-0700 (PDT)